System and method of adaptive congestion management

ABSTRACT

A system and method for adaptive congestion management uses measurements of usage of each edge network segment both per-customer and for each edge network segment as a whole. The technique then processes these measurements to determine what changes to make to allowed transfer rates to alleviate congestion. Adaptive congestion management also uses these measurements to determine when to remove any restrictions on transfer rates in the case that the edge network segment is no longer congested.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 61/500,851 entitled SYSTEM AND METHOD OF ADAPTIVE CONGESTIONMANAGEMENT, filed on Jun. 24, 2011, which is incorporated herein byreference in its entirety.

FIELD

The present disclosure relates to congestion management for broadbanddata services. In particular, the present disclosure relates to adaptivecongestion management on edge network segments.

BACKGROUND

Physical network infrastructures used by a broadband data serviceproviders generally includes edge network segments to which customerdevices are attached. In the cable television industry, the edge networksegments include a Hybrid Fiber Coaxial physical infrastructure overwhich data is transferred using a DOCSIS set of standards. In thetelecommunications industry, the edge network segments include DigitalSubscriber Loop (DSL) connections between Broadband Remote AccessServers (BRAS) and the customer device(s). In both of these industries,edge network segments using WiFi access standards are also beingdeployed.

Digital service providers allocate bandwidth based on anover-subscription model in which the sum of all the allowed maximum datatransfer rates for all customers attached to a physical segment greatlyexceeds the actual available bandwidth of the segment. Suchover-subscription models rely on statistical multiplexing based onassumptions that not all customers actively transfer data at the sametime, for example. However, customer demand for more bandwidth persegment is increasing rapidly due in large part to the availability ofcontent over the Internet such as video.

As customer demand for bandwidth rapidly increases, edge networksegments are becoming increasingly congested. In some areas, the demandfor additional bandwidth on a network edge segment exceeds the bandwidthavailable on the segment. When this occurs, customers may not haveaccess to their desired amount of bandwidth. This reduction in bandwidthfor each customer can cause customer dissatisfaction resulting lostrevenue for the digital service providers.

Digital service providers may need to incur substantial costs to addcapacity or may use a number of congestion management techniques tomitigate congestion on edge network segments. For example, providers mayuse active management techniques which reduce the permitted bandwidthfor a subset of customers who are using the greatest amount ofbandwidth. This technique restricts a relatively small number ofcustomers to benefit the remaining customers.

Currently used active management techniques may not be optimal forcongestion management and can be ineffective. For example, one commoncongestion management technique reduces a maximum allowed transfer ratefor customers with high usage of the segment for a period of time. Thistechnique reduces the maximum allowed rate be a fixed amount, which maybe based on a provisioned maximum allowed rate for a customer. Thistechnique can be ineffective, for example, when congestion is severeenough that the customers are only achieving less than the reducedmaximum allowed rate. In that case the rate change has no effect.Another congestion management technique enforces maximum allowed ratesfor various types of digital traffic. However, this technique does notdirectly measure the congestion state of edge network segments and maytherefore result in too much or too little rate limiting for optimalcongestion management.

SUMMARY

In illustrative embodiments, systems and methods for adaptive congestionmanagement for broadband data services are disclosed herein. Inparticular, the systems and methods disclosed herein relate to adaptivecongestion management of edge network segments where the physicalnetwork infrastructure may be heavily over-subscribed.

In an illustrative embodiment, adaptive congestion management improveson existing congestion management techniques. Adaptive congestionmanagement uses measurements of usage of each edge network segment bothper-customer and for each edge network segment as a whole. The techniquethen processes these measurements to determine what changes to make toallowed transfer rates to alleviate congestion. Adaptive congestionmanagement also uses these measurements to determine when to remove anyrestrictions on transfer rates in the case that the edge network segmentis no longer congested.

In an illustrative embodiment, a method for adaptive congestionmanagement is disclosed herein. The method includes gathering periodicmeasurement data of usage of an edge network segment and determiningwhen the edge network segment crosses a threshold from uncongested tocongested. When the edge segment crosses the threshold from uncongestedto congested, the method includes determining a calculated profile forcustomers on the edge segment, and computing a restricted transfer ratecorresponding to each of one or more of the customers. The restrictedtransfer rate can then be applied to each of the one or more of thecustomers to reduce the congestion on the edge segment.

In another illustrative embodiment, when the edge network segmentcrosses back over the threshold from congested to uncongested, themethod includes determining if customers on the edge segment have therestricted transfer rate. If customers do have the restricted transferrate, the method includes removing the restricted transfer rate from atleast a subset of those customers.

In an illustrative embodiment, a broadband network service is deliveredby a broadband service provider (provider) using a physical networkinfrastructure. However, the systems and methods disclosed herein can beapplied to various different types of data services in which a resourcemanagement system can measure per-customer/subscriber usage andper-physical segment usage.

BRIEF DESCRIPTION OF THE DRAWINGS

The systems and methods disclosed herein are illustrated in the figuresof the accompanying drawings which are meant to be exemplary and notlimiting, in which like references are intended to refer to like orcorresponding parts.

FIG. 1 illustrates an embodiment of a provider network infrastructure.

FIG. 2 illustrates a block diagram of an embodiment of a method ofadaptive congestion management for an edge network segment that crossesfrom uncongested to congested.

FIG. 3 illustrates a block diagram of an embodiment of a method ofadaptive congestion management for an edge network segment that crossesfrom congested to uncongested.

DETAILED DESCRIPTION

Detailed embodiments of the systems and methods for congestionmanagement for broadband data services are disclosed herein, however, itis to be understood that the disclosed embodiments are merely exemplaryof the systems and methods, which may be embodied in various forms.Therefore, specific functional details disclosed herein are not to beinterpreted as limiting, but merely as a basis for the claims and as arepresentative basis for teaching one skilled in the art to variouslyemploy the systems and methods disclosed herein.

Adaptive Congestion Management

Systems and methods for adaptive congestion management for broadbanddata services are disclosed herein. Adaptive congestion managementimproves on existing congestion management techniques. A generalprovider network infrastructure according to an illustrative embodimentis described with reference to FIG. 1. As illustrated in FIG. 1, aprovider network 102 includes one or more edge network segments 104(also referred to herein as “segment” and edge segments) at an edge ofthe provider network to which one or more customer devices 106 isattached.

In an illustrative embodiment, adaptive congestion management usesmeasurements of usage of each edge network segment 104 both per-customerand per-segment as a whole. The technique then processes thesemeasurements to determine what changes to make to allowed transfer ratesto alleviate congestion. Adaptive congestion management also uses thesemeasurements to determine when to remove any restrictions on transferrates in the case that the edge network segment 104 is no longercongested.

In an illustrative embodiment, a resource management system 108, incommunication with the provider network 102, implements the adaptivecongestion management functions. The resource management system 108 cangather the measurement data from the provider network 102 using variousexisting network management protocols, such as but not limited to:DOCSIS IPDR, which is generally used in the cable industry; RADIUS,DIAMETER, which is generally used in the wireline, mobile industries andWiFi; SNMP, which is in general use; and Netflow, which is in generaluse to measure characteristics of discrete data transfers usually usingsampling.

The above protocols allow periodic measurement of the per-customer andper-segment usage data. The above protocols also allow active control ofthe per-customer allowed transfer rates by using the protocol to setattributes of the transfer policing technique in use on the edge networksegment 104.

Generally, the transfer policing for a customer is implemented by theequipment that provides the edge network segment 104 data transfer usinga token bucket mechanism. Such a token bucket generally has a number ofattributes. The token bucket may have a maximum rate attribute, whichmay define a token fill rate for the token bucket and wherein themaximum sustained transfer rate is usually expressed in bits per second.The token bucket may have a burst size attribute, wherein a depth of thetoken bucket is usually expressed in bytes. The token bucket may have aburst rate attribute, which may define a maximum transfer rate allowedwhen draining the bucket of accumulated tokens. The burst rate attributemay not be present in all implementations. If the burst rate attributeis not implemented then it can be limited by the maximum possibletransfer rate of the edge network segment.

Typically associated with each per-customer transfer token bucket is aqueue to which packets received by the edge equipment for a customer arequeued awaiting transfer when allowed by the token bucket. The queueusually has attributes that determine the maximum number of bytes orpackets or both that are allowed to be queued (the queue depth). If thequeue depth is met or exceeded then a received packet is discarded.

In an illustrative embodiment, the maximum rate establishes the rate atwhich data can be transferred to a customer when the token bucket isempty. During periods when the received data rate for a customer is lessthan the maximum rate, the token bucket accumulates unused tokens up tothe burst size. If the received rate then exceeds the maximum rate for aperiod then the transfer can be done at the burst rate while the tokenbucket is non-empty. Once the token bucket is emptied, the transfer ratedrops back to the maximum rate.

When data is received and has to be queued before it is allowed to besent because the token bucket is empty, then some edge equipmentimplementations count each such packet that arrives as a “delay” packet.The protocols for measurement described earlier allow the resourcemanagement system 108 to gather those delay packet counts in addition tothe other usage data. The protocols also allow the resource managementsystem 108 to gather the count of discarded packets.

In addition to the per-customer policing step above, the edge segmentequipment may also limit the aggregate transfer rate for the edgenetwork segment 104 to its maximum possible rate. Limiting the aggregatetransfer rate for the edge network segment 104 is typically done byeffectively reducing the rate at which data is taken from all tokenbuckets. This has the effect on a congested edge network segment ofreducing the achieved transfer rate for all customers that have dataready to be transferred (and allowed by their token bucket). This hasthe undesirable effect of reducing the achieved rate for all customersregardless of the effect that it may have which varies by applicationdata type. For example, a well-known application type that uses hightransfer rates for sustained periods of time is file transfer. A secondwell-known application type that uses lower transfer rates for sustainedperiods of time is video data transfer. A third well-known applicationtype that uses variable transfer rates for short periods of time isinteractive Internet Web browsing. From the customer's perspective, filetransfer is not particularly sensitive to having its allowed transferrate reduced; video transfer rate can be reduced such that the qualityof the video resolution is reduced and this is preferable for customerexperience rather than discarding data; Web transfer is sensitive totransfer rate since it directly affects the customer's experience as thecustomer waits for Web pages to be displayed.

Segment Congestion Measurement

In an illustrative embodiment, one or more of the protocols describedabove is used by the resource management system 108 to gather periodicmeasurements of the usage of each edge network segment 104. In addition,the resource management system 108 can use one or more of thoseprotocols to obtain the characteristics of each edge network segment104, such as the maximum transfer rate (bandwidth). Such periodicmeasurements can be obtained frequently, for example, every minute.

Using the recent history of measurements for each edge network segment104 the resource management system 108 can decide whether each edgenetwork segment 104 is congested or not. Since the timescale forcontrolling the allowed rate for a customer is typically longer than thesegment measurement rate, it is not usually preferred to simply use thelast per-minute segment measurement to make the congested decision.

Instead, in an illustrative embodiment a method for deciding thecongestion state uses an Exponentially Weighted Moving Average (EWMA) ofthe recent history of measurements. In this embodiment, parameters areconfigured which determine the operation of the EWMA method, such as butnot limited to a Weighting factor, which is a multiplier to apply to thecurrent EWMA value. The Weighting factor is always a value less than 1.By adjusting the Weighting factor the EWMA computed value at each newmeasurement period can be made more or less sensitive to oldermeasurements.

In an illustrative embodiment, using EWMA at each measurement interval,the resource management system 108 calculates the new EWMA value foreach edge network segment 104. Then using a configurable attribute forthe congestion threshold, such as a percentage of the maximum possibletransfer rate of an edge network segment 104, and using the actualmaximum possible transfer rate for an edge network segment 104 obtainedusing one of the protocols, the resource management system 108calculates whether the EWMA usage value exceeds the configured thresholdor not.

In another illustrative embodiment, two separate EWMA usage values arecomputed and maintained for each an edge network segment 104. The firstEWMA is used to determine when an edge network segment 104 crosses fromuncongested to congested. While the second EWMA is used to determinewhen an edge network segment 104 crosses from congested to uncongested.In this embodiment, the second EWMA has a larger weighting factor suchthat it is affected by a longer time span of measurements than the firstEWMA. This has the effect of the detection of the uncongested tocongested threshold crossing being faster with respect to recent historythan the detection of the congested to uncongested threshold crossing.

Customer Congestion Management

In an illustrative embodiment, when an edge network segment 104 has beendetermined to be congested by the method described above, the resourcemanagement system 108 uses further per-customer calculations using theper-customer measurements to determine which customers to activelymanage by reducing their transfer parameters. In addition, the resourcemanagement system 108 calculates, from the measurement data, the usageprofile for each customer. The profile is the pattern to which thatcustomer's recent history conforms.

The per-customer calculations use one or more inputs. The inputs mayinclude, but are not limited to: recent history of usage measurements,for example transferred bytes and packets; recent history of delayed anddiscarded packets, for example when available from the edge equipment;and provisioned transfer parameters for the customer, which may beobtained by the protocols described above or through configuration. Theinputs may also include configured parameters that determine per-profilereduced transfer parameters when a customer is actively managed. Theconfigured parameters may include a maximum rate factor, such as amultiplier (e.g. a percentage) to apply to the measured rate which thendetermines the new maximum rate value to apply. The configuredparameters may include a burst size, such as a new burst size to apply.The configured parameters may also include a burst rate factor, such asa multiplier (e.g. a percentage) to apply to the measured rate whichthen determines the new burst rate value to apply.

Profile Calculation

In an illustrative embodiment, the per-customer data measurementperiodicity is about 5 minutes, but may be up to about 15 minutes. Inorder to calculate the profile to which a customer's usage conforms, theresource management system 108 uses the recent history of usage data andconfigured match parameters that define the characteristics of eachprofile. The profiles and match parameters may include but are notlimited to FileTransfer and Video. For example, the profiles and matchparameters may include a FileTransfer. The FileTransfer may include aminimum actual rate, such as a threshold of achieved transfer rate abovewhich the pattern matches FileTransfer; a minimum delay ratio, such as athreshold ratio of actual delayed packets above which the patternmatches FileTransfer, wherein the actual delay ratio is computed fromthe delay packet count divided by the total packet count; and/or a timespan, such as a duration of recent measurement data to use for matching.

In another example, the profiles and match parameters may include aVideo_(—)1. The Video_(—)1 may include a minimum actual rate, such as athreshold of achieved transfer rate above which the pattern matchesHDVideo; a maximum delay ratio, such as a threshold ratio of actualdelayed packets below which the pattern matches HDVideo, wherein theactual delay ratio is computed from the delay packet count divided bythe total packet count; and/or a time span, such as a duration of recentmeasurement data to use for matching.

In an illustrative embodiment, other Video profiles can be defined byassigning different match parameter values. For Video profiles theactual transfer rate is relatively constant over the time duration ofthe video content, for example, 90 minutes for a full movie. Inaddition, for each video resolution there is generally a specificrelatively constant rate.

In an illustrative embodiment, the resource management system 108computes the profile (if any) that the customer actual usage patternmatches using the above parameters. It does so each time there is newperiodic usage measurement data available.

Segment Crosses Uncongested to Congested Threshold

A method of adaptive congestion management for an edge network segmentthat crosses from uncongested to congested according to an illustrativeembodiment is described with reference to FIG. 2. As described above,the resource management system gathers periodic measurements of theusage of each edge network segment, illustrated as block 202, anddetermines whether each edge network segment is congested or not,illustrated as block 204. Additionally, as described above, the resourcemanagement system can calculate usage profiles for each customer,illustrated as block 206.

In an illustrative embodiment, when the edge network segment isdetermined by the resource management system to have become congested,the resource management system then takes the calculated profile foreach customer on that edge network segment, illustrated as block 208, todetermine what changes to make to allowed transfer rates and for whichset of customers.

In an illustrative embodiment, the resource management system implementsthe following algorithm or methodology:

On the first period at which the congestion threshold is crossed, takethe set of FileTransfer profile customers, illustrated as block 208. Foreach such customer, compute a new maximum allowed rate for thecustomers, illustrated as block 210. The new maximum allowed rate may becalculated by taking the actual measured data rate over the profile timespan and multiplying by the maximum rate factor. This yields a newmaximum allowed rate that is less than the current rate. Then apply thenew maximum allowed rate to each such customer, illustrated as block212. The new maximum allowed rate may be applied to each such customerusing a protocol, as described above, to communicate that change withthe edge equipment. In an alternative algorithm or method, a new burstrate can be computed by multiplying the actual measured data rate overthe profile time span by the burst rate factor. The new burst rate andthe burst size are then also set along with the new maximum allowedrate.

On subsequent segment usage measurement periods, if the edge networksegment remains congested, then take the set of Video profile customers,illustrated as block 208. For each such customer compute a new maximumallowed rate, illustrated as block 210. The a new maximum allowed ratemay be calculated by taking the actual measured data rate over theprofile time span and multiplying by the maximum rate factor. Thisyields a new maximum allowed rate that is less than the current rate.Then apply the new maximum allowed rate to each such customer,illustrated as block 212. The new maximum allowed rate may be applied toeach such customer using a protocol, as described above, to communicatethat change with the edge equipment.

Segment Crosses Congested to Uncongested Threshold

A method of adaptive congestion management for an edge network segmentthat crosses from congested to uncongested according to an illustrativeembodiment is described with reference to FIG. 3. As described above,the resource management system gathers periodic measurements of theusage of each edge network segment, illustrated as block 302, anddetermines whether each edge network segment is congested or not,illustrated as block 304. Additionally, as described above, the resourcemanagement system can calculate usage profiles for each customer,illustrated as block 306.

In an illustrative embodiment, when the resource management systemdetermines that the edge network segment has become uncongested, asdescribed above, the resource management system obtains the calculatedprofile for the customers on the uncongested edge network segment,illustrated as block 308. The resource management system then takes theset of profiled customers that have reduced transfer parameter settings,illustrated as block 310, and removes the reduced transfer parametersettings applied to one or more of the profiled customers, illustratedas block 312. The removal is performed using a protocol, as describedabove, to communicate with the edge equipment to make the changes.

In an illustrative embodiment, the resource management system implementsthe following algorithm or methodology to avoid the possibility ofimmediately causing congestion:

On the first period when the segment congestion measurement calculationdetermines that the edge network segment has now become uncongested,take a set of profiled Video customers that have reduced rates applied,illustrated as block 310. If that set is empty then take a set ofFileTransfer customers that have reduced rates applied, illustrated asblock 310. With the set so derived, select a random subset, for example,by choosing a fraction of the set at random. Then remove the reducedrate restriction for that selected subset, illustrated as block 312.

On subsequent periods, while the edge network segment remainsuncongested, compute a new set of customers, as described above, andagain take a random subset of those and remove the rate restrictions forthat subset. In this embodiment, the subset at each step is selectedusing the same fraction parameter such that as the algorithm iterates ateach segment measurement period, the algorithm exponentially reduces theset of remaining reduced rate customers.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. A machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory and executed by a processor unit. Memory may beimplemented within the processor unit or external to the processor unit.As used herein the term “memory” refers to types of long term, shortterm, volatile, nonvolatile, or other memory and is not to be limited toa particular type of memory or number of memories, or type of media uponwhich memory is stored.

If implemented in firmware and/or software, the functions may be storedas one or more instructions or code on a computer-readable medium.Examples include computer-readable media encoded with a data structureand computer-readable media encoded with a computer program.Computer-readable media includes physical computer storage media. Astorage medium may be an available medium that can be accessed by acomputer. By way of example, and not limitation, such computer-readablemedia can include RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage or other magnetic storage devices, orother medium that can be used to store desired program code in the formof instructions or data structures and that can be accessed by acomputer; disk and disc, as used herein, includes compact disc (CD),laser disc, optical disc, digital versatile disc (DVD), floppy disk andblu-ray disc where disks usually reproduce data magnetically, whilediscs reproduce data optically with lasers. Combinations of the aboveshould also be included within the scope of computer-readable media.

Although the present disclosure and its advantages have been describedin detail, it should be understood that various changes, substitutionsand alterations can be made herein without departing from the technologyof the disclosure as defined by the appended claims. Moreover, the scopeof the present application is not intended to be limited to theparticular configurations of the process, machine, manufacture,composition of matter, means, methods and steps described in thespecification. As one of ordinary skill in the art will readilyappreciate from the disclosure, processes, machines, manufacture,compositions of matter, means, methods, or steps, presently existing orlater to be developed that perform substantially the same function orachieve substantially the same result as the correspondingconfigurations described herein may be utilized according to the presentdisclosure. Accordingly, the appended claims are intended to includewithin their scope such processes, machines, manufacture, compositionsof matter, means, methods, or steps.

While the systems and methods for congestion management for broadbanddata services have been disclosed and illustrated in connection withcertain embodiments, many variations and modifications will be evidentto those skilled in the art and may be made without departing from thespirit and scope of the disclosure. The disclosure is thus not to belimited to the precise details of methodology or construction set forthabove as such variations and modification are intended to be includedwithin the scope of the disclosure.

What is claimed is:
 1. A method for adaptive congestion managementcomprising: determining when data usage on an edge network segmentcrosses a first threshold of data usage; and adjusting a data transferrate for one or more customers on said edge network segment by an amountpredetermined for said one or more customers when said data usagecrosses the first threshold of data usage.
 2. The method of claim 1,further comprising: gathering periodic measurements of data on said edgenetwork segment; determining when said data usage crosses the firstthreshold of data usage based on said periodic measurements.
 3. Themethod of claim 2, further comprising: determining a customer profilefor the one or more customers; and adjusting the data transfer rate forthe one or more customers based on the customer profile.
 4. The methodof claim 3, further comprising: determining the customer profile for theone or more customers based on per-customer measurements.
 5. The methodof claim 3, wherein adjusting the data rate comprises: restricting thedata transfer rate for the one or more customers when the data usage onsaid edge segment crosses the first threshold from a congested rate tocongested an uncongested rate.
 6. The method of claim 3, whereinadjusting the data rate comprises: removing a restriction of the datatransfer rate for the one or more customers when the data usage on saidedge network segment crosses a second threshold from an uncongested rateto a congested rate.
 7. The method of claim 1, further comprising:determining the first threshold based on a history of measurements forsaid edge network segment.
 8. An apparatus for adaptive congestionmanagement comprising: means for determining when data usage on an edgenetwork segment crosses a first threshold of data usage; means foradjusting a data transfer rate by an amount predetermined for one ormore customers on said edge network segment when said data usage crossesthe first threshold of data usage.
 9. The apparatus of claim 8, furthercomprising: means for gathering periodic measurements of data on thenetwork segment; means for determining when said data usage crosses thefirst threshold of data usage based on said periodic measurements. 10.The apparatus of claim 9, further comprising: means for determining acustomer profile for the one or more customers; and means for adjustingthe data transfer rate for the one or more customers based on thecustomer profile.
 11. The apparatus of claim 10, further comprising:means for determining the customer profile for the one or more customersbased on per-customer measurements.
 12. The apparatus of claim 10,further wherein the means for adjusting the data rate comprises: meansfor restricting the data transfer rate for the one or more customerswhen the data usage on said edge segment crosses the first thresholdfrom a congested rate to congested an uncongested rate.
 13. Theapparatus of claim 10, wherein the means for adjusting the data ratecomprises: means for removing a restriction of the data transfer ratefor the one or more customers when the data usage on said edge networksegment crosses a second threshold from an uncongested rate to acongested rate.
 14. The apparatus of claim 8, further comprising: meansfor determining the first threshold based on a history of measurementsfor said edge network segment.
 15. A non-transitory computer readablemedium storing computer executable instructions for: determining whendata usage on an edge network segment crosses a first threshold of datausage; and adjusting a data transfer rate by an amount predetermined forone or more customers on said edge network segment when said data usagecrosses the first threshold of data usage.
 16. The non-transitorycomputer readable media of claim 15, also storing computer executableinstructions for: gathering periodic measurements of data on the networksegment; and determining when said data usage crosses the firstthreshold of data usage based on said periodic measurements.
 17. Thenon-transitory computer readable media of claim 15, also storingcomputer executable instructions for: calculating a customer profile forthe one or more customers; and adjusting the data transfer rate for theone or more customers based on the customer profile.
 18. Thenon-transitory computer readable media of claim 15, also storingcomputer executable instructions for: determining the first thresholdbased on a history of measurements for said edge network segment.
 19. Amethod for adaptive congestion management comprising: gathering periodicmeasurement data of usage of an edge segment of a network; determiningwhen said edge segment crosses a threshold from uncongested tocongested; determining a calculated profile for customers on said edgesegment; computing a restricted transfer rate corresponding to each ofone or more of said customers; and applying said restricted transferrate to each of said one or more of said customers.
 20. A method foradaptive congestion management comprising: gathering periodicmeasurement data of usage of an edge segment of a network; determiningwhen said edge segment crosses a threshold from congested touncongested; determining if customers on said edge segment have arestricted transfer rate; and removing said restricted transfer ratefrom at least a subset of those said customers that have said restrictedtransfer rate.